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Writing Smart Programs
Anoop Thomas Mathew
twitter: atmb4u
Profoundis Inc.
History
then:
low level → high level
now:
code driven → data driven
What is Smart
everyone makes mistakes
○ looking at past to avoid future mistakes
everyone misses what comes next
○ predict...
Jargon Buster
Dataset
Data Cleaning
Dimension
Model
Training
Parameters
Dimensionality Reduction
Accuracy
Overfitting
Unde...
Parameter
Optimization
Heuristic / Statistical / Machine Learning
- “know parameters well”
Eg: stock market prediction dis...
Supervised vs Unsupervised
we know what we want
vs.
find what’s interesting
NB: training data, accuracy, semi-supervised
Classification / Regression / Clustering
★ rain prediction
★ digit recognition
★ customer segmentation
★ time-series predi...
The ML Process
plan → collect → execute → test
time: 50% 30% 5-10% 15-20%
DEMO 1
SHOPPING PREDICT
(https://github.com/atmb4u/smarter-apps-2016)
DEMO 2
Support Vector Machine
(https://github.com/atmb4u/smarter-apps-2016)
Dummy Tasks
★ user auto-login redirect
★ predict if a user will convert to paid
Thank You
Follow me on twitter:@atmb4u
Writing Smarter Applications with Machine Learning
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Writing Smarter Applications with Machine Learning Slide 1 Writing Smarter Applications with Machine Learning Slide 2 Writing Smarter Applications with Machine Learning Slide 3 Writing Smarter Applications with Machine Learning Slide 4 Writing Smarter Applications with Machine Learning Slide 5 Writing Smarter Applications with Machine Learning Slide 6 Writing Smarter Applications with Machine Learning Slide 7 Writing Smarter Applications with Machine Learning Slide 8 Writing Smarter Applications with Machine Learning Slide 9 Writing Smarter Applications with Machine Learning Slide 10 Writing Smarter Applications with Machine Learning Slide 11 Writing Smarter Applications with Machine Learning Slide 12 Writing Smarter Applications with Machine Learning Slide 13
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Introduction to Machine Learning Terminologies for beginners

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Writing Smarter Applications with Machine Learning

  1. Writing Smart Programs Anoop Thomas Mathew twitter: atmb4u Profoundis Inc.
  2. History then: low level → high level now: code driven → data driven
  3. What is Smart everyone makes mistakes ○ looking at past to avoid future mistakes everyone misses what comes next ○ predicting what to expect clusters of items do exist ○ automatically group things based on similarity
  4. Jargon Buster Dataset Data Cleaning Dimension Model Training Parameters Dimensionality Reduction Accuracy Overfitting Underfitting Testing Domain
  5. Parameter Optimization Heuristic / Statistical / Machine Learning - “know parameters well” Eg: stock market prediction disaster; no. of lawyers vs. no. of suicides
  6. Supervised vs Unsupervised we know what we want vs. find what’s interesting NB: training data, accuracy, semi-supervised
  7. Classification / Regression / Clustering ★ rain prediction ★ digit recognition ★ customer segmentation ★ time-series prediction ★ spam filtering Algorithm Examples ★ K-means ★ SVR ★ SVC ★ Naive Bayes ★ Random Forest Decision Tree
  8. The ML Process plan → collect → execute → test time: 50% 30% 5-10% 15-20%
  9. DEMO 1 SHOPPING PREDICT (https://github.com/atmb4u/smarter-apps-2016)
  10. DEMO 2 Support Vector Machine (https://github.com/atmb4u/smarter-apps-2016)
  11. Dummy Tasks ★ user auto-login redirect ★ predict if a user will convert to paid
  12. Thank You Follow me on twitter:@atmb4u
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Introduction to Machine Learning Terminologies for beginners

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